Genes & CellsGenes & CellsГены и КлеткиGenes and Cells2313-18292500-2562Human Stem Cells Institute56284310.23868/gc562843Original Study ArticlesОригинальные исследованияResearch ArticleTopological data analysis suggests human brain network reconfiguration during the transition from resting state to cognitive loadТопологический анализ позволяет предположить реконфигурацию сетей мозга человека при переходе от состояния покоя к когнитивной нагрузкеhttps://orcid.org/0009-0001-8465-83733968-9518ErnstonIlia M.ЭрнстонИлья Максимович
Russian Federation
ilya.ernston@gmail.comhttps://orcid.org/0000-0002-7811-58314081-5605OnuchinArsenii A.ОнучинАрсений Андреевич
Russian Federation
onuchinaa@my.msu.ruhttps://orcid.org/0000-0003-1571-91923897-2897AdamovichTimofey V.АдамовичТимофей Валерьевич
Russian Federation
tadamovich11@gmail.comPsychological Institute of the Russian Academy of EducationПсихологический институт Российской академии образованияCenter for Neurophysics and Neuromorphic TechnologiesЦентр нейрофизики и нейроморфных технологийSkolkovo Institute of Science and TechnologyСколковский институт науки и технологий21112023151220231844334462607202308112023Copyright ©; 2023, Eco-VectorCopyright ©; 2023, Эко-Вектор2023Eco-VectorЭко-Векторhttps://creativecommons.org/licenses/by-nc-nd/4.0/https://genescells.ru/2313-1829/article/view/562843

BACKGROUND: Neural networks of the brain continually adapt to changing environmental demands. The network approach in neuroscience, which focuses on the analysis of structural and functional network characteristics related to cognitive functions, is a highly promising avenue for understanding the psychophysiological mechanisms underlying the adaptive dynamics of cognitive processes.

AIM: We aimed to explore how the topological features of functional connectomes in the human brain are linked to different cognitive demands. The focus was on understanding the dynamic changes in brain networks during working memory tasks to identify network characteristics inherent to working memory.

METHODS: We examined the topological characteristics of functional brain networks in the resting state and cognitive load provided by the execution of the Sternberg Item Recognition Paradigm based on electroencephalographic data. Electroencephalogram traces from 67 healthy adults were processed to estimate functional connectivity using the coherence method. We propose that the topological properties of functional networks in the human brain are distinct between cognitive load and resting state, with higher integration in the networks during cognitive load.

RESULTS: The topological features of functional connectomes depend on the current state of cognitive processing and change with task-induced cognitive load variation. Moreover, functional connectivity during working memory tasks showed a faster emergence of homology group generators, supporting the idea of a relationship between the initial stages of working memory execution and an increase in faster network integration, with connector hubs playing a crucial role.

CONCLUSION: Collected evidence suggest that cognitive states, particularly those related to working memory, are associated with distinct topological properties of functional brain networks, highlighting the importance of network dynamics in cognitive processing.

Обоснование. Адаптация нейронных сетей мозга к переменным условиям окружающей среды — ключевой аспект эффективного исполнения когнитивных функций. Сетевой подход в нейронауке, фокусирующийся на анализе структурных и функциональных характеристик сетей, которые связаны с когнитивными функциями, является весьма многообещающим направлением для понимания психофизиологических механизмов, лежащих в основе адаптивной динамики когнитивных процессов.

Цель исследования — изучить, как топологические особенности функциональных коннектомов мозга человека связаны с осуществлением различных когнитивных процессов. Основное внимание было уделено определению динамических изменений в мозговых сетях во время выполнения задач на рабочую память с целью выявления сетевых характеристик, присущих сетям при исполнении этой когнитивной функции.

Методы. На основе электроэнцефалографических данных подробно рассмотрены топологические характеристики функциональных мозговых сетей в состоянии покоя и при когнитивной нагрузке, обеспечиваемой выполнением теста Стернберга на рабочую память (Sternberg Item Recognition Paradigm). Записи ЭЭГ 67 здоровых взрослых были обработаны для оценки функциональной связности с помощью метода когерентности. Мы предполагаем, что топологические свойства функциональных сетей в человеческом мозге различаются между когнитивной нагрузкой и состоянием покоя с более высокой интеграцией в сетях во время когнитивной нагрузки.

Результаты. Исследование подтверждает, что топологические особенности функциональных коннектомов зависят от текущего состояния когнитивной обработки и изменяются в ответ на изменения когнитивной нагрузки, вызванной заданием. Анализ также продемонстрировал, что функциональные коннектомы, зафиксированные при выполнении задач на рабочую память, характеризуются более быстрым появлением генераторов групп гомологии. Это подтверждает идею взаимосвязи между начальными этапами выполнения задач на рабочую память и увеличением скорости сетевой интеграции, при этом решающую роль играют соединительные хабы (connector hubs).

Заключение. Различные уровни когнитивной нагрузки, в частности при задачах на рабочую память, связаны с разными топологическими свойствами функциональных сетей мозга, что подчёркивает важность сетевой динамики в когнитивной обработке.

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